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Abstract

Currently, there is a certain fluctuation in the real estate industry, so it is particularly important to analyze the solvency of real estate enterprises. In order to find a reliable model suitable for studying the difference in house prices, this study collects the research data through data collection, and uses the K-means clustering method to construct the corresponding model as a basic research in combination with the machine learning research method. At the same time, this paper compares the analysis effects of several common machine learning models and finds the advantages and disadvantages of these methods through mathematical statistics. In addition, combined with practice, this paper constructs a nonlinear generalized additive model, and based on machine learning technology, validates the validity of the model based on data analysis, the collected predictors. In view of the improvement of the solvency of real estate enterprises, diversified operation of real estate enterprises can maintain reasonable cash flow and make up for the defect of poor liquidity of real estate. Furthermore, this paper uses the stability method to find the optimal model. In addition, the generalized additive model effectively reveals the complex nonlinear relationship between continuous predictors and house prices. Through research, it can be seen that the nonlinear generalized additive model based on machine learning can play an important role in real estate industry forecasting and has certain theoretical reference significance for subsequent related research.

Keywords

Real estate generalized additive model machine learning K-means Algorithm

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Analysis of debt-paying ability of real estate enterprises based on fuzzy mathematics and K-means algorithm

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